'''
Pytorch implementation of ResNet models.

Reference:
[1] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, 2016.
'''
import torch
import torch.nn as nn
import torch.nn.functional as F


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, in_planes, planes, stride=1):
        super(BasicBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)

        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion*planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(self.expansion*planes)
            )

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        out += self.shortcut(x)
        out = F.relu(out)
        return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, in_planes, planes, stride=1):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, self.expansion*planes, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(self.expansion*planes)

        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion*planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(self.expansion*planes)
            )

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = F.relu(self.bn2(self.conv2(out)))
        out = self.bn3(self.conv3(out))
        out += self.shortcut(x)
        
        out = F.relu(out)
        return out


class ResNet(nn.Module):
    def __init__(self, block, num_blocks, num_classes=10, temp=1.0):
        super(ResNet, self).__init__()
        self.in_planes = 64

        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
        self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
        self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
        self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
        self.fc = nn.Linear(512*block.expansion, num_classes)
        self.avgpool = nn.AdaptiveAvgPool2d(1)
        self.temp = temp

    def _make_layer(self, block, planes, num_blocks, stride):
        strides = [stride] + [1]*(num_blocks-1)
        layers = []
        for stride in strides:
            layers.append(block(self.in_planes, planes, stride))
            self.in_planes = planes * block.expansion
        return nn.Sequential(*layers)

    def forward_feature(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = F.avg_pool2d(out, 4)
        out = out.view(out.size(0), -1)
        
        return out

    def forward_logit(self, x):
        x = F.relu(self.bn1(self.conv1(x)))
        # x = self.maxpool(x)

        x_level = []
        x = self.layer1(x)
        x_level.append(x)
        x = self.layer2(x)
        x_level.append(x)
        x = self.layer3(x)
        x_level.append(x)

        x = self.layer4(x)

        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        logits = []
        for i in range(3):
            x_logit = self.avgpool(x_level[i])
            x_logit = x_logit.view(x_logit.size(0), -1)
            logits.append(x_logit)
        
        logits.append(x)

        # x = self.fc(x) / self.temp

        return logits

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = F.avg_pool2d(out, 4)
        out = out.view(out.size(0), -1)
        out = self.fc(out) / self.temp
        return out


def resnet18(temp=1.0, **kwargs):
    model = ResNet(BasicBlock, [2, 2, 2, 2], temp=temp, **kwargs)
    return model


def resnet34(temp=1.0, **kwargs):
    model = ResNet(BasicBlock, [3, 4, 6, 3], temp=temp, **kwargs)
    return model


def resnet50(temp=1.0, **kwargs):
    model = ResNet(Bottleneck, [3, 4, 6, 3], temp=temp, **kwargs)
    return model


def resnet101(temp=1.0, **kwargs):
    model = ResNet(Bottleneck, [3, 4, 23, 3], temp=temp, **kwargs)
    return model


def resnet110(temp=1.0, **kwargs):
    model = ResNet(Bottleneck, [3, 4, 26, 3], temp=temp, **kwargs)
    return model


def resnet152(temp=1.0, **kwargs):
    model = ResNet(Bottleneck, [3, 8, 36, 3], temp=temp, **kwargs)
    return model
